Focusing on graph-based state representation leads to other promising works. In [1]}, a representation named DeepWalk is introduced that generates a sequence of nodes using random walk. This model is used in natural language processing and trains a model similar to word2vec for representing a graph. In [2]}, an asymmetric proximity preserving graph embedding based on random walk is developed that can be used to represent a graph for social network problems like link prediction.
